thankyou so much for uploading these videos, your explanations are easily understandable

Such clear explanation

your channel is highly underrated, it deserves a lot more audience

@CodingLane

Жыл бұрын

Thank you for this considerate comment 😇

Hand's down,best explanation ever:)

@CodingLane

2 жыл бұрын

Haha… Thanks you so much 😄

Hello thanks for the info. But you didn't mention the purpose of the square for the gradient.

waiting for SVM since you explain so nicely..thnks

@CodingLane

2 жыл бұрын

Thank you! I will upload SVM video after finishing RNN series

You are the best, thanks dude 🤙

@CodingLane

2 жыл бұрын

You’re welcome 😇

Hi Sir, any plan of uploading videos on support vector machines? If yes then, please try to cover the mathematical background of SVM as much as you can ... Anyway your content is really appreciable...Thanks !

@CodingLane

2 жыл бұрын

Thank you so much for your suggestion! Yes, I will be making video on SVM and covering mathematical details behind it.

how to initialize value of Sdw and Sdb?

You're incredible

@CodingLane

2 жыл бұрын

Thank You Marc! Glad you found my videos valuable.

good

If situation with w and b would be opposite values of gradients on the vertical axis were small and values on horizontal axis where large would RMSprop slow down the training by making vertical axis values larger and horizontal axis values smaller?

@CodingLane

2 жыл бұрын

No no… it will still make the training faster. Vertical horizontal is just an example i am giving. Realistically, it can be in any direction. In every direction, its gonna work the same way.

Hi，Is it correct that you set the vertical coordinates to w and the horizontal coordinates to b? I think it should be the other way around.Because whether the goal can be reached in the end depends on w rather than b.

@CodingLane

Жыл бұрын

Hi… neither we set vertical to w nor b. Its just an example given… in a model.. there are many axis, not just x and y if we have more than 2 number of features. So a model can take any axis as any w or b. and it doesn’t matter as well which axis is for waht

@ueslijtety

Жыл бұрын

@@CodingLane thanks!So in practice this is not going to be a 2D planar image but a multidimensional image?And which parameters can determine the point of convergence in gradient descent?W OR b?

@minister1005

10 ай бұрын

So i guess what he means is that if you get a high gradient, you will be updated a lower amount and if you get a low gradient, you will be updated a higher amount.

What is (dw)^2?

What is S?

Man and what kind of LOSS should I use when training using RMSprop optimizer?

@CodingLane

Жыл бұрын

You can use any loss function

Explain ADMM also